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Artificial Intelligence Technologies and Employee Pay in the United Kingdom : Evidence From Matched Employer–Employee Data

Schulz, Felix LU orcid ; Valizade, Danat ; Stuart, Mark ; Skordis, Jolene and Soffia, Magdalena (2025) In British Journal of Industrial Relations
Abstract
This paper examines the impact of artificial intelligence (AI)-enabled technologies on employee pay in the United Kingdom. We use matched nationally representative data from the Employers’ Digital Practices at Work Survey and an original survey of 6000 UK workers and apply machine learning techniques to uncover relationships between AI technology and employee pay across qualification and occupation skill groups. We find that lower skilled workers were the primary beneficiaries of AI, but this effect was contingent on the extent of worker interaction with AI. Further analysis shows that employee involvement in pay determination facilitates a more equitable distribution of AI-related pay benefits by enabling a significant uplift in pay among... (More)
This paper examines the impact of artificial intelligence (AI)-enabled technologies on employee pay in the United Kingdom. We use matched nationally representative data from the Employers’ Digital Practices at Work Survey and an original survey of 6000 UK workers and apply machine learning techniques to uncover relationships between AI technology and employee pay across qualification and occupation skill groups. We find that lower skilled workers were the primary beneficiaries of AI, but this effect was contingent on the extent of worker interaction with AI. Further analysis shows that employee involvement in pay determination facilitates a more equitable distribution of AI-related pay benefits by enabling a significant uplift in pay among lower qualified workers. Overall, while the implications of AI for pay outcomes are broadly positive, the study highlights the need to strengthen workplace voice mechanisms to ensure a more equitable distribution of benefits from the growing use of AI. (Less)
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type
Contribution to journal
publication status
published
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in
British Journal of Industrial Relations
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:105020429311
ISSN
1467-8543
DOI
10.1111/bjir.70019
language
English
LU publication?
yes
id
8612b405-3a0b-46d1-83d9-b4c7fe2d318e
alternative location
https://onlinelibrary.wiley.com/journal/14678543
date added to LUP
2025-11-05 10:50:32
date last changed
2025-11-06 04:00:11
@article{8612b405-3a0b-46d1-83d9-b4c7fe2d318e,
  abstract     = {{This paper examines the impact of artificial intelligence (AI)-enabled technologies on employee pay in the United Kingdom. We use matched nationally representative data from the Employers’ Digital Practices at Work Survey and an original survey of 6000 UK workers and apply machine learning techniques to uncover relationships between AI technology and employee pay across qualification and occupation skill groups. We find that lower skilled workers were the primary beneficiaries of AI, but this effect was contingent on the extent of worker interaction with AI. Further analysis shows that employee involvement in pay determination facilitates a more equitable distribution of AI-related pay benefits by enabling a significant uplift in pay among lower qualified workers. Overall, while the implications of AI for pay outcomes are broadly positive, the study highlights the need to strengthen workplace voice mechanisms to ensure a more equitable distribution of benefits from the growing use of AI.}},
  author       = {{Schulz, Felix and Valizade, Danat and Stuart, Mark and Skordis, Jolene and Soffia, Magdalena}},
  issn         = {{1467-8543}},
  language     = {{eng}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{British Journal of Industrial Relations}},
  title        = {{Artificial Intelligence Technologies and Employee Pay in the United Kingdom : Evidence From Matched Employer–Employee Data}},
  url          = {{http://dx.doi.org/10.1111/bjir.70019}},
  doi          = {{10.1111/bjir.70019}},
  year         = {{2025}},
}